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Topic modeling for expert finding using latent Dirichlet allocation
Author(s) -
Momtazi Saeedeh,
Naumann Felix
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1102
Subject(s) - latent dirichlet allocation , computer science , topic model , ranking (information retrieval) , information retrieval , probabilistic logic , context (archaeology) , rank (graph theory) , task (project management) , learning to rank , field (mathematics) , data mining , artificial intelligence , machine learning , mathematics , biology , pure mathematics , economics , paleontology , management , combinatorics
The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation (LDA) to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference (TREC) Enterprise track for 2005 and 2006 show that the proposed topic‐based approach outperforms the state‐of‐the‐art profile‐ and document‐based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic‐based approach to the improved document‐based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion. This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Data Concepts Technologies > Classification Technologies > Machine Learning